Noise reduced color image using panchromatic image

ABSTRACT

A method for producing a noise-reduced digital color image, includes providing an image having panchromatic pixels and color pixels corresponding to at least two color photoresponses; providing from the image a panchromatic image and at least one color image; and using the panchromatic image and the color image to produce the noise-reduced digital color image by setting a plurality of color characteristics equal to the corresponding panchromatic characteristics at each color pixel location.

CROSS REFERENCE RELATED APPLICATION

This application is a continuation of prior U.S. patent application Ser.No. 11/752,484, filed May 23, 2007, which is hereby incorporated hereinby reference in its entirety.

Reference is made to commonly-assigned U.S. Ser. No. 11/558,571(Publication No. 2008/0112612), filed Nov. 10, 2006, of James E. Adams,Jr., et al., entitled “NOISE REDUCTION OF PANCHROMATIC AND COLOR IMAGE”.

FIELD OF THE INVENTION

The invention relates generally to the field of digital image processingoperations that produce a full-color noise-reduced full-resolution imagefrom an image having panchromatic and color pixels.

BACKGROUND OF THE INVENTION

One of the most common and frequently essential image processingoperations is noise reduction. This is especially true for digital stilland video camera images that may have been captured under insufficientlighting conditions. One way to address digital image capture under lessthan optimum lighting conditions is to either acquire or synthesize oneor more color channels that are particularly sensitive to low orinsufficient scene illumination. The data from the channels withincreased light sensitivity are generally used to guide the subsequentimage processing of the data from the accompanying standard colorchannels. Noise reduction is a prime candidate for benefiting from thisadditional image data. A number of examples exist in the literature.U.S. Pat. No. 6,646,246 (Gindele, et al.) teaches using an extendeddynamic range color filter array (CFA) pattern with slow and fastpixels, noise-cleaning the slow pixel data using only slow pixel dataand noise-cleaning the fast pixel data using only fast pixel data. Thisapproach achieved noise reduction at the expense of image resolution aseach color channel is now subdivided into a fast channel and a slowchannel and the subsequent merger can produce image processing artifactsmore troublesome than the addressed original noise. U.S. Pat. No.7,065,246 (Xiaomang, et al.) is representative of a fair number ofsimilarly disclosed inventions in that it reveals constructing aluminance signal from directly sensed color channel data, in this casecyan, magenta, yellow, and green. The high-frequency component of theconstructed luminance is used to replace the high-frequency component ofthe original color channel signals to affect a net noise reduction ofthe image data. Although somewhat effective, the major liability of thisapproach is that the synthesized luminance channel is constructed fromnoisy color channel components resulting in an essentially equally noisysynthetic channel.

A suggestion of a better approach can be found in U.S. Pat. No.5,264,924 (Cok). Cok discloses direct measurement of red, green, blue,and luminance values at each pixel location. The high-frequencyluminance data which is designed to be inherently less noisy than thecorresponding high-frequency red, green, and blue data is used toreplace said high-frequency red, green, and blue data to producenoise-cleaned red, green, and blue signals. Since the vast majority ofdigital still and video cameras use a single sensor equipped with a CFAthat only senses one color channel per pixel, Cok cannot be directlypracticed in such systems.

Although Xiaomang and Cok describe luminance signals, a color channelwith photometric sensitivity conforming to the luminance channel of thehuman visual system may be unnecessarily restrictive.

SUMMARY OF THE INVENTION

It is an object of the present invention to produce a noise-reducedfull-resolution full-color image from a digital image havingpanchromatic and color pixels.

This object is achieved by a method for producing a noise-reduceddigital color image, comprising:

a. providing an image having panchromatic pixels and color pixelscorresponding to at least two color photoresponses;

b. providing from the image a panchromatic image and at least one colorimage; and

c. using the panchromatic image and the color image to produce thenoise-reduced digital color image by setting a plurality of colorcharacteristics equal to the corresponding panchromatic characteristicsat each color pixel location.

It is a feature of the present invention that images can be capturedunder low-light conditions with a sensor having panchromatic and colorpixels and processing reduces noise in a full-color image produced fromthe panchromatic and colored pixels. A more useful signal can becaptured with a more general panchromatic channel, which has highersensitivity at all wavelengths over the luminance channel of the humanvisual system.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a perspective of a computer system including a digital camerafor implementing the present invention;

FIG. 2 is a block diagram of a preferred embodiment of the presentinvention;

FIG. 3 is a block diagram of an alternate embodiment of the presentinvention;

FIG. 4 is a block diagram of an alternate embodiment of the presentinvention;

FIG. 5 is a block diagram of an alternate embodiment of the presentinvention;

FIG. 6 is a region of pixels used in block 202 in FIG. 2;

FIG. 7 is a region of pixels used in block 206 in FIG. 2;

FIG. 8 is a region of pixels used in block 210 in FIG. 3; and

FIG. 9 is a region of pixels used in block 204 in FIG. 4.

DETAILED DESCRIPTION OF THE INVENTION

In the following description, a preferred embodiment of the presentinvention will be described in terms that would ordinarily beimplemented as a software program. Those skilled in the art will readilyrecognize that the equivalent of such software can also be constructedin hardware. Because image manipulation algorithms and systems are wellknown, the present description will be directed in particular toalgorithms and systems forming part of, or cooperating more directlywith, the system and method in accordance with the present invention.Other aspects of such algorithms and systems, and hardware or softwarefor producing and otherwise processing the image signals involvedtherewith, not specifically shown or described herein, can be selectedfrom such systems, algorithms, components and elements known in the art.Given the system as described according to the invention in thefollowing materials, software not specifically shown, suggested ordescribed herein that is useful for implementation of the invention isconventional and within the ordinary skill in such arts.

Still further, as used herein, the computer program can be stored in acomputer readable storage medium, which can comprise, for example;magnetic storage media such as a magnetic disk (such as a hard drive ora floppy disk) or magnetic tape; optical storage media such as anoptical disc, optical tape, or machine readable bar code; solid stateelectronic storage devices such as random access memory (RAM), or readonly memory (ROM); or any other physical device or medium employed tostore a computer program.

Before describing the present invention, it facilitates understanding tonote that the present invention is preferably utilized on any well-knowncomputer system, such as a personal computer. Consequently, the computersystem will not be discussed in detail herein. It is also instructive tonote that the images are either directly input into the computer system(for example by a digital camera) or digitized before input into thecomputer system (for example by scanning an original, such as a silverhalide film).

Referring to FIG. 1, there is illustrated a computer system 110 forimplementing the present invention. Although the computer system 110 isshown for the purpose of illustrating a preferred embodiment, thepresent invention is not limited to the computer system 110 shown, butcan be used on any electronic processing system such as found in homecomputers, kiosks, retail or wholesale photofinishing, or any othersystem for the processing of digital images. The computer system 110includes a microprocessor-based unit 112 for receiving and processingsoftware programs and for performing other processing functions. Adisplay 114 is electrically connected to the microprocessor-based unit112 for displaying user-related information associated with thesoftware, e.g., by a graphical user interface. A keyboard 116 is alsoconnected to the microprocessor based unit 112 for permitting a user toinput information to the software. As an alternative to using thekeyboard 116 for input, a mouse 118 can be used for moving a selector120 on the display 114 and for selecting an item on which the selector120 overlays, as is well known in the art.

A compact disk-read only memory (CD-ROM) 124, which typically includessoftware programs, is inserted into the microprocessor based unit forproviding a way of inputting the software programs and other informationto the microprocessor based unit 112. In addition, a floppy disk 126 canalso include a software program, and is inserted into themicroprocessor-based unit 112 for inputting the software program. Thecompact disk-read only memory (CD-ROM) 124 or a floppy disk 126 canalternatively be inserted into an externally located disk drive unit 122which is connected to the microprocessor-based unit 112. Still further,the microprocessor-based unit 112 can be programmed, as is well known inthe art, for storing the software program internally. Themicroprocessor-based unit 112 can also have a network connection 127,such as a telephone line, to an external network, such as a local areanetwork or the Internet. A printer 128 can also be connected to themicroprocessor-based unit 112 for printing a hardcopy of the output fromthe computer system 110.

Images can also be displayed on the display 114 via a personal computercard (PC card) 130, such as, as it was formerly known, a PCMCIA card(based on the specifications of the Personal Computer Memory CardInternational Association) that contains digitized images electronicallyembodied in the PC card 130. The PC card 130 is ultimately inserted intothe microprocessor-based unit 112 for permitting visual display of theimage on the display 114. Alternatively, the PC card 130 can be insertedinto an externally located PC card reader 132 connected to themicroprocessor-based unit 112. Images can also be input via the compactdisk 124, the floppy disk 126, or the network connection 127. Any imagesstored in the PC card 130, the floppy disk 126 or the compact disk 124,or input through the network connection 127, can have been obtained froma variety of sources, such as a digital camera (not shown) or a scanner(not shown). Images can also be input directly from a digital camera 134via a camera docking port 136 connected to the microprocessor-based unit112 or directly from the digital camera 134 via a cable connection 138to the microprocessor-based unit 112 or via a wireless connection 140 tothe microprocessor-based unit 112.

In accordance with the invention, the algorithm can be stored in any ofthe storage devices heretofore mentioned and applied to images in orderto interpolate sparsely populated images.

FIG. 2 is a high level diagram of a preferred embodiment. The digitalcamera 134 is responsible for creating an original digitalred-green-blue-panchromatic (RGBP) color filter array (CFA) image 200,also referred to as the digital RGBP CFA image or the RGBP CFA image. Itis noted at this point that other color channel combinations, such ascyan-magenta-yellow-panchromatic, can be used in place ofred-green-blue-panchromatic in the following description. The key itemis the inclusion of a panchromatic channel. This image is considered tobe a sparsely sampled image because each pixel in the image containsonly one pixel value of red, green, blue, or panchromatic data. Apanchromatic image interpolation block 202 produces a full-resolutionpanchromatic image 204. At this point in the image processing chain eachcolor pixel location has an associated panchromatic value and either ared, green, or blue value. With the help of the full-resolutionpanchromatic image the noise associated with the red, green, and bluepixel values is now reduced in an RGB CFA image noise reduction block210 to produce a noise-reduced RGB CFA image 212. An RGB CFA imageinterpolation block 214 subsequently produces a noise-reducedfull-resolution full-color image 226.

FIG. 3 is a high level diagram of a second preferred embodiment. Thedigital camera 134 is responsible for creating an original digitalred-green-blue-panchromatic (RGBP) color filter array (CFA) image 200,also referred to as the digital RGBP CFA image or the RGBP CFA image. Itis noted at this point that other color channel combinations, such ascyan-magenta-yellow-panchromatic, can be used in place ofred-green-blue-panchromatic in the following description. The key itemis the inclusion of a panchromatic channel. This image is considered tobe a sparsely sampled image because each pixel in the image containsonly one pixel value of red, green, blue, or panchromatic data. Apanchromatic image interpolation block 202 produces a full-resolutionpanchromatic image 204. At this point in the image processing chain eachcolor pixel location has an associated panchromatic value and either ared, green, or blue value. With the help of the full-resolutionpanchromatic image the noise associated with the red, green, and bluepixel values is now reduced in an RGB CFA image noise reduction block210 to produce a noise-reduced RGB CFA image 212. An RGB CFA imageinterpolation block 214 subsequently produces a noise-reducedfull-resolution full-color image 216. Finally, a full-resolutionfull-color noise reduction block 218 produces a final noise-reducedfull-resolution full-color image block 224.

FIG. 4 is a high level diagram of a preferred embodiment. The digitalcamera 134 is responsible for creating an original digitalred-green-blue-panchromatic (RGBP) color filter array (CFA) image 200,also referred to as the digital RGBP CFA image or the RGBP CFA image. Itis noted at this point that other color channel combinations, such ascyan-magenta-yellow-panchromatic, can be used in place ofred-green-blue-panchromatic in the following description. The key itemis the inclusion of a panchromatic channel. This image is considered tobe a sparsely sampled image because each pixel in the image containsonly one pixel value of red, green, blue, or panchromatic data. Apanchromatic image interpolation block 202 produces a full-resolutionpanchromatic image 204. At this point in the image processing chain eachcolor pixel location has an associated panchromatic value and either ared, green, or blue value. Next a full-resolution panchromatic imagenoise reduction block 206 produces a noise-reduced full-resolutionpanchromatic image 208. With the help of the noise-reducedfull-resolution panchromatic image the noise associated with the red,green, and blue pixel values is now reduced in an RGB CFA image noisereduction block 210 to produce a noise-reduced RGB CFA image 212. An RGBCFA image interpolation block 214 subsequently produces a noise-reducedfull-resolution full-color image 222.

FIG. 5 is a high level diagram of a preferred embodiment. The digitalcamera 134 is responsible for creating an original digitalred-green-blue-panchromatic (RGBP) color filter array (CFA) image 200,also referred to as the digital RGBP CFA image or the RGBP CFA image. Itis noted at this point that other color channel combinations, such ascyan-magenta-yellow-panchromatic, can be used in place ofred-green-blue-panchromatic in the following description. The key itemis the inclusion of a panchromatic channel. This image is considered tobe a sparsely sampled image because each pixel in the image containsonly one pixel value of red, green, blue, or panchromatic data. Apanchromatic image interpolation block 202 produces a full-resolutionpanchromatic image 204. At this point in the image processing chain eachcolor pixel location has an associated panchromatic value and either ared, green, or blue value. Next a full-resolution panchromatic imagenoise reduction block 206 produces a noise-reduced full-resolutionpanchromatic image 208. With the help of the noise-reducedfull-resolution panchromatic image the noise associated with the red,green, and blue pixel values is now reduced in an ROB CFA image noisereduction block 210 to produce a noise-reduced RGB CFA image 212. An RGBCFA image interpolation block 214 subsequently produces a noise-reducedfull-resolution full-color image 216. Finally, a full-resolutionfull-color noise reduction block 218 produces a final noise-reducedfull-resolution full-color image block 220.

Returning to FIG. 2, panchromatic image interpolation block 202 can beperformed in any appropriate way known to those skilled in the art. Twoexamples are now given. Referring to FIG. 6, one way to estimate apanchromatic value for pixel X₅ is to simply average the surrounding sixpanchromatic values, i.e.:

X ₅=(P ₁ +P ₂ +P ₃ +P ₇ +P ₈ +P ₉)/6

Alternate weighting to the pixel value in this approach are also wellknown to those skilled in the art. As an example,

X ₅=(P ₁+2P ₂ +P ₃ +P ₇+2P ₈ +P ₉)/8

Alternately, an adaptive approach can be used by first computing theabsolute values of directional gradients (absolute directionalgradients).

B ₅ =|P ₁ −P ₉|

V ₅ =|P ₂ −P ₈|

S ₅ =|P ₃ −P ₇|

The value of X₅ is now determined by one of three two-point averages.

BX ₅=(P ₁ +P ₉)/2

VX ₅=(P ₂ +P ₈)/2

SX ₅=(P ₃ +P ₇)/2

The two-point average associated with the smallest value of the set ofabsolute direction gradients is used for computing X₅, e.g., if V₅≦B₅and V₅≦S₅, then X₅=VX₅.

Returning to FIG. 2, RGB CFA image noise reduction block 210 isperformed by setting a plurality of color characteristics equal to thecorresponding panchromatic characteristics at each color pixel location.Any number of such characteristics will be known to those skilled in thearts. As an example, referring to FIG. 8, a first spatial red pixeldifference, also known as a directional gradient, from pixel R₅ can bedefined within the shown neighborhood in eight different directions:

R₁−R₅ (north-west)

R₂−R₅ (north)

R₃−R₅ (north-east)

R₄−R₅ (west)

R₆−R₅ (east)

R₇−R₅ (south-west)

R₈−R₅ (south)

R₉−R₅ (south-east).

Another example of a color characteristic is the second spatial pixeldifference. Again referring to FIG. 8, a second red pixel differencefrom pixel R₅ can be given within the shown neighborhood in fourdifferent directions:

2R₅−R₁−R₉ (backslash)

2R₅−R₂−R₈ (vertical)

2R₅−R₃−R₇ (slash)

2R₅−R₄−R₆ (horizontal).

Four examples of the invention are now given. Referring to FIG. 7, it isto be noted that each pixel location has an associated panchromaticvalue. It is also noted that the number and placement of interveningnon-red pixels (i.e., blank pixels in FIG. 7) is irrelevant. All that isrequired is a central pixel value with at least one or more neighboringpixel values of the same color. It is also noted that even though FIG. 7depicts a square pixel neighborhood that is 5 pixels high and 5 pixelswide, any size and shape neighborhood may be used. Also, in thefollowing discussion, green or blue would be substituted for red whencleaning the green or blue color channels. Since the panchromaticchannel is faster than the red channel, the panchromatic channel hasless noise than the red channel and setting the first spatial red pixeldifference averaged over a valid pixel neighborhood equal to the firstspatial panchromatic pixel difference averaged over the same pixelneighborhood reduces the noise of the red pixel R₅. This is accomplishedby computing the following weighted average.

R ₅ [c ₁(P ₁ −P ₅ +R ₁)+c ₂(P ₂ −P ₅ +R ₂)+c ₃(P ₃ −P ₅ +R ₃)+c ₄(P ₄ −P₅ +R ₄)+c ₅(P ₅ −P ₅ +R ₅)+c ₆(P ₆ −P ₅ +R ₆)+c ₇(P ₇ −P ₅ +R ₇)+c ₈(P ₈−P ₅ +R ₈)+c ₉(P ₉ −P ₅ +R ₉)]/(c ₁ +c ₂ +c ₃ +c ₄ +c ₅ +c ₆ +c ₇ +c ₈+c ₉)

The weighting coefficients c₁ through c₉ define a valid pixelneighborhood and are computed from differences in panchromatic valuesand from differences in red values.

c ₁=1 if |P ₁ −P ₅ |≦t _(p) and |R ₁ −R ₅ |≦t _(r), otherwise c ₁=0

c ₂=1 if |P ₂ −P ₅ |≦t _(p) and |R ₂ −R ₅ |≦t _(r), otherwise c ₂=0

c ₃=1 if |P ₃ −P ₅ |≦t _(p) and |R ₃ −R ₅ |≦t _(r), otherwise c ₃=0

c ₄=1 if |P ₄ −P ₅ |≦t _(p) and |R ₄ −R ₅ |≦t _(r), otherwise c ₄=0

c ₅=1 if |P ₅ −P ₅ |≦t _(p) and |R ₅ −R ₅ |≦t _(r), otherwise c ₅=0

c ₆=1 if |P ₆ −P ₅ |≦t _(p) and |R ₆ −R ₅ |≦t _(r), otherwise c ₆=0

c ₇=1 if |P ₇ −P ₅ |≦t _(p) and |R ₇ −R ₅ |≦t _(r), otherwise c ₇=0

c ₈=1 if |P ₈ −P ₅ |≦t _(p) and |R ₈ −R ₅ |≦t _(r), otherwise c ₈=0

c ₉=1 if |P ₉ −P ₅ |≦t _(p) and |R ₉ −R ₅ |≦t _(r), otherwise c ₉=0

In these expressions t_(p) and t_(r) are predetermined positivethreshold values that are chosen to exclude pixel values that areseparated from the central pixel (R₅) by any edges in the pixelneighborhood shown in FIG. 7. It is noted that by these definition c₅ isalways 1; this is to assure that we always include at least one pixel inthe summation. Alternate schemes for populating the weightingcoefficients, and for choosing the valid neighborhoods, may be used andcan be adapted to using full-resolution panchromatic data. For example,the weighting coefficients may be calculated such that they take on anyvalue between zero and one.

An alternate method to the one described above is to use the secondspatial pixel difference averaged over a valid pixel neighborhood. Againreferring to FIG. 7, setting the second spatial red pixel differenceaveraged over a valid pixel neighborhood equal to second spatialpanchromatic pixel difference averaged over the same pixel neighborhoodreduces the noise of the red pixel R₅.

R ₅ =[d ₁(−P ₁+2P ₅ −P ₉ +R ₁ +R ₉)/2+d ₂(−P ₂+2P ₅ −P ₈ +R ₂ +R ₈)/2+d₃(−P ₃+2P ₅ −P ₇ +R ₃ +R ₇)/2+d ₄(−P ₄+2P ₅ −P ₆ +R ₄ +R ₆)/2+R ₅]/(d ₁+d ₂ +d ₃ +d ₄+1)

The weighting coefficients d₁ through d₄ define a valid pixelneighborhood and are computed from differences in panchromatic valuesand from differences in red values.

d ₁=1 if |P ₁ −P ₅ |≦t _(p) and |P ₉ −P ₅ |≦t _(p) and

|R ₁ −R ₅ |≦t _(r) and |R ₉ −R ₅ |≦t _(r), otherwise d ₁=0

d ₂=1 if |P ₂ −P ₅ |≦t _(p) and |P ₈ −P ₅ |≦t _(p) and

|R ₂ −R ₅ |≦t _(r) and |R ₈ −R ₅ |≦t _(r), otherwise d ₂=0

d ₃=1 if |P ₃ −P ₅ |≦t _(p) and |P ₇ −P ₅ |≦t _(p) and

|R ₃ −R ₅ |≦t _(r) and |R ₇ −R ₅ |≦t _(r), otherwise d ₃=0

d ₄=1 if |P ₄ −P ₅ |≦t _(p) and |P ₆ −P ₅ |≦t _(p) and

|R ₄ −R ₅ |≦t _(r) and |R ₆ −R ₅ |≦t _(r), otherwise d ₄=0

In these expressions t_(p) and t_(r) are predetermined positivethreshold values that are chosen to exclude pixel values that areseparated from the central pixel (R₅) by any edges in the pixelneighborhood shown in FIG. 7. It is noted that by these definition allthe weighting functions can be zero simultaneously; to assure that wealways include at least one pixel in the summation R₅ is added to thenumerator and 1 is added to the denominator on the weighted sum above.An alternate method is to use the median of all the first spatial pixeldifferences in a valid pixel neighborhood. Again referring to FIG. 7,setting the median of all the first spatial red pixel differences in avalid pixel neighborhood equal to the median of all the first spatialpanchromatic pixel differences in the same pixel neighborhood reducesthe noise of the red pixel R₅. First, one panchromatic and one red9-point median values are computed.

P _(M)=median[(P ₁ −P ₅), (P ₂ −P ₅), (P ₃ −P ₅),

(P ₄ −P ₅), (P ₅ −P ₅), (P ₆ −P ₅),

(P ₇ −P ₅), (P ₈ −P ₅), (P ₉ −P ₅)]

R _(M)=median[(R ₁ −R ₅), (R ₂ −R ₅), (R ₃ −R ₅),

(R ₄ −R ₅), (R ₅ −P ₅), (R ₆ −R ₅),

(R ₇ −R ₅), (R ₈ −R ₅), (R ₉ −R ₅)]

R_(M) is set equal to P_(M) and, noting that adding or subtracting aconstant from all the terms inside the median operator does not changethe order of the terms, the expression is solved for R₅.

R ₅=median(R ₁ , R ₂ , R ₃ , R ₄ , R ₅ , R ₆ , R ₇ , R ₈ , R ₉)−median(P₁ , P ₂ , P ₃ , P ₄ , P ₅ , P ₆ , P ₇ , P ₈ , P ₉)+P ₅.

An alternate method is to simultaneously use both the first and secondspatial pixel differences averaged over a valid pixel neighborhood.Again referring to FIG. 7, setting the average of the first and secondspatial red pixel differences averaged over a valid pixel neighborhoodequal to the average of the first and second spatial panchromatic pixeldifferences averaged over the same pixel neighborhood reduces the noiseof the red pixel R₅.

R ₅ =[c ₁(P ₁ −P ₅ +R ₁)+c ₂(P ₂ −P ₅ +R ₂)+c ₃(P ₃ −P ₅ +R ₃)+c ₄(P ₄−P ₅ +R ₄)+c ₅(P ₅ −P ₅ +R ₅)+c ₆(P ₆ −P ₅ +R ₆)+c ₇(P ₇ −P ₅ +R ₇)+c₈(P ₈ −P ₅ +R ₈)+c ₉(P ₉ −P ₅ +R ₉)]/2(c ₁ +c ₂ +c ₃ +c ₄ +c ₅ +c ₆ +c ₇+c ₈ +c ₉)+[d ₁(−P ₁+2P ₅ −P ₉ +R ₁ +R ₉)/2+d ₂(−P ₂+2P ₅ −P ₈ +R ₂ +R₈)/2+d ₃(−P ₃+2P ₅ −P ₇ +R ₃ +R ₇)/2+d ₄(−P ₄+2P ₅ −P ₆ +R ₄ +R ₆)/2+R₅]/2(d ₁ +d ₂ +d ₃ +d ₄+1)

The weighting coefficients c₁ through c₉ and d₁ through d₄ are the sameas above. The average of the first and second spatial pixel differencescan also be replaced by a weighted average, and the weights may beeither fixed or calculated according to, for example, how close R₅ is toan edge.

Alternate methods to the four examples discussed above include using themaximum of the first spatial pixel differences, the minimum of the firstspatial pixel differences, and an adaptive directional median filter.Still more alternate methods are possible by utilizing otherpanchromatic characteristics that are known to those skilled in the artin conjunction with other well-known noise reduction methods such as,but not limited to, infinite impulse response (IIR) filtering andsingular value decomposition (SVD).

It is also well known by those skilled in the art that pixelneighborhoods such as depicted in FIG. 7 can result from Laplacian orGaussian pyramid decompositions or wavelet decompositions of the imagedata. By simultaneously applying the same decomposition methods to thefull-resolution panchromatic data, each resulting pixel within each RGBimage decomposition component will still have an associated panchromaticvalue. Therefore, the foregoing discussion and examples remain relevantand unaltered.

Returning to FIG. 2, RGB CFA image interpolation block 214 can beperformed using any of the well-known CFA interpolation or demosaickingtechniques described in the prior art. U.S. Pat. No. 5,852,468 (Okada)describes a typical non-adaptive method while U.S. Pat. No. 5,506,619(Adams, et al.) teaches a representative adaptive approach.

In FIG. 3, full-resolution full-color noise reduction block 216 can beperformed in a manner similar to the RGB CFA image noise reduction block210, only in block 216 there are red, green, blue, and panchromaticvalues at each pixel location. Therefore, noise reduction can beperformed using contiguous pixel neighborhoods, such as depicted in FIG.8. When referenced to FIG. 8, the examples given above of the averagefirst spatial pixel difference, average second spatial pixel difference,and median first spatial pixel difference are directly applicable.

In FIG. 4, full-resolution panchromatic image noise reduction block 206would be performed using any of the well-known methods in the prior artfor grayscale or single-channel image noise reduction. Referring to FIG.9, a sigma filter can be used to accomplish noise reduction of thepanchromatic pixel P₅. This is accomplished by computing the followingweighted average.

P ₅(cP ₁ +c ₂ P ₂ +c ₃ P ₃ +c ₄ P ₄ +c ₅ P ₅ +c ₆ P ₆ +c ₇ P ₇ +c ₈ P ₈+c ₉ P ₉)/(c ₁ +c ₂ +c ₃ +c ₄ +c ₅ +c ₆ +c ₇ +c ₈ +c ₉)

The weighting coefficients c₁ through c₉ are computed from differencesin panchromatic values.

c ₁=1 if |P ₁ −P ₅ |≦t, otherwise c ₁=0

c ₂=1 if |P ₂ −P ₅ |≦t, otherwise c ₂=0

c ₃=1 if |P ₃ −P ₅ |≦t, otherwise c ₃=0

c ₄=1 if |P ₄ −P ₅ |≦t, otherwise c ₄=0

c ₅=1 if |P ₅ −P ₅ |≦t, otherwise c ₅=0

c ₆=1 if |P ₆ −P ₅ |≦t, otherwise c ₆=0

c ₇=1 if |P ₇ −P ₅ |≦t, otherwise c ₇=0

c ₈=1 if |P ₈ −P ₅ |≦t, otherwise c ₈=0

c ₉=1 if |P ₉ −P ₅ |≦t, otherwise c ₉=0

In these expressions t is a predetermined threshold value that is chosento exclude pixel values that are separated from the central pixel (P₅)by any edges in the pixel neighborhood shown in FIG. 9. It is noted thatby these definition c₅ is always 1; this is to assure that we alwaysinclude at least one pixel in the summation. Alternate schemes forpopulating sigma filter weighting coefficients are well known in theart. An alternate method is to use an adaptive median filter. Againreferring to FIG. 9, four panchromatic are computed.

P _(H)=median(P ₄ , P ₅ , P ₆)

P _(B)=median(P ₁ , P ₅ , P ₉)

P _(V)=median(P ₂ , P ₅ , P ₈)

P _(S)=median(P ₃ , P ₅ , P ₇)

The noise-reduced value for P₅ corresponds to the panchromatic medianvalue that is closest to original panchromatic value associated with P₅.

P ₅ =P _(H) if |P _(H) −P ₅ |≦{|P _(B) −P ₅ |, |P _(V) −P ₅ |, |P _(S)−P ₅|}

P ₅ =P _(B) if |P _(B) −P ₅ |≦{|P _(H) −P ₅ |, |P _(V) −P ₅ |, |P _(S)−P ₅|}

P ₅ =P _(V) if |P _(V) −P ₅ |≦{|P _(H) −P ₅ |, |P _(B) −P ₅ |, |P _(S)−P ₅|}

P ₅ =P _(S) if |P _(S) −P ₅ |≦{|P _(H) −P ₅ |, |P _(B) −P ₅ |, |P _(V)−P ₅|}

Alternate schemes for employing adaptive median filters are well knownin the art and can be used.

In addition to the methods described above, other well-known noisereduction methods such as, but not limited to, infinite impulse response(IIR) filtering and singular value decomposition (SVD) could be used.

It is also well known by those skilled in the art that pixelneighborhoods such as depicted in FIG. 9 can result from Laplacian orGaussian pyramid decompositions or wavelet decompositions of the imagedata. Therefore, the foregoing discussion and examples remain relevantand unaltered.

The noise reduction algorithms disclosed in the preferred embodiments ofthe present invention can be employed in a variety of user contexts andenvironments. Exemplary contexts and environments include, withoutlimitation, wholesale digital photofinishing (which involves exemplaryprocess steps or stages such as film in, digital processing, printsout), retail digital photofinishing (film in, digital processing, printsout), home printing (home scanned film or digital images, digitalprocessing, prints out), desktop software (software that appliesalgorithms to digital prints to make them better -or even just to changethem), digital fulfillment (digital images in—from media or over theweb, digital processing, with images out—in digital form on media,digital form over the web, or printed on hard-copy prints), kiosks(digital or scanned input, digital processing, digital or scannedoutput), mobile devices (e.g., PDA or cell phone that can be used as aprocessing unit, a display unit, or a unit to give processinginstructions), and as a service offered via the World Wide Web.

In each case, the noise reduction algorithms can stand alone or can be acomponent of a larger system solution. Furthermore, the interfaces withthe algorithm, e.g., the scanning or input, the digital processing, thedisplay to a user (if needed), the input of user requests or processinginstructions (if needed), the output, can each be on the same ordifferent devices and physical locations, and communication between thedevices and locations can be via public or private network connections,or media based communication. Where consistent with the foregoingdisclosure of the present invention, the algorithms themselves can befully automatic, can have user input (be fully or partially manual), canhave user or operator review to accept/reject the result, or can beassisted by metadata (metadata that can be user supplied, supplied by ameasuring device (e.g. in a camera), or determined by an algorithm).Moreover, the algorithms can interface with a variety of workflow userinterface schemes.

The noise reduction algorithms disclosed herein in accordance with theinvention can have interior components that utilize various datadetection and reduction techniques (e.g., face detection, eye detection,skin detection, flash detection).

The invention has been described in detail with particular reference tocertain preferred embodiments thereof, but it will be understood thatvariations and modifications can be effected within the spirit and scopeof the invention.

PARTS LIST

-   110 Computer System-   112 Microprocessor-based Unit-   114 Display-   116 Keyboard-   118 Mouse-   120 Selector on Display-   122 Disk Drive Unit-   124 Compact Disk-read Only Memory (CD-ROM)-   126 Floppy Disk-   127 Network Connection-   128 Printer-   130 Personal Computer Card (PC card)-   132 PC Card Reader-   134 Digital Camera-   136 Camera Docking Port-   138 Cable Connection-   140 Wireless Connection-   200 RGBP CFA Image-   202 Panchromatic Image Interpolation-   204 Full-Resolution Panchromatic Image-   206 Full-Resolution Panchromatic Image Noise Reduction-   208 Noise-Reduced Full-Resolution Panchromatic Image-   210 RGB CFA Image Noise Reduction-   212 Noise-Reduced RGB CFA Image-   214 RGB CFA Image Interpolation-   216 Noise-Reduced Full-Resolution Full-Color Image-   218 Full-Resolution Full-Color Noise Reduction-   220 Final Noise-Reduced Full-Resolution Full-Color Image-   222 Noise-Reduced Full-Resolution Full-Color Image-   224 Final Noise-Reduced Full-Resolution Full-Color Image-   226 Noise-Reduced Full-Resolution Full-Color Image

1. A method for providing a noise-reduced value for a color pixel from adigital image having panchromatic pixels and color pixels, comprising:a. providing a first image having sparsely sampled panchromatic pixelsand color pixels corresponding to at least two color photoresponses; b.interpolating the panchromatic pixels to provide a full-resolutionpanchromatic image; and c. providing a noise-reduced value for aparticular color pixel in the first image, by: i. selecting aneighborhood of color pixels surrounding and including the particularcolor pixel, each selected color pixel having a value and having thesame color photoresponse as the particular color pixel; ii. selecting aneighborhood of panchromatic pixels from the full-resolutionpanchromatic image, each selected panchromatic pixel having a value andbeing associated by location with a color pixel in the selectedneighborhood of color pixels; and iii. using the values of the pixelsfrom the selected neighborhoods of color and panchromatic pixels toprovide the noise reduced value for the particular color pixel.
 2. Themethod of claim 1 wherein step c(iii) includes using means or medianscalculated from the values of the pixels from the selected neighborhoodsto provide the noise-reduced value for the particular color pixel. 3.The method of claim 2 wherein the means or medians are means or mediansof directional gradients.
 4. The method of claim 3 wherein the gradientsare weighted.